A compilation of soybean ESTs: generation and analysis
Bibliographic record
Abstract
Whole-genome sequencing is fundamental to understanding the genetic composition of an organism. Given the size and complexity of the soybean genome, an alternative approach is targeted random-gene sequencing, which provides an immediate and productive method of gene discovery. In this study, more than 120000 soybean expressed sequence tags (ESTs) generated from more than 50 cDNA libraries were evaluated. These ESTs coalesced into 16928 contigs and 17336 singletons. On average, each contig was composed of 6 ESTs and spanned 788 bases. The average sequence length submitted to dbEST was 414 bases. Using only those libraries generating more than 800 ESTs each and only those contigs with 10 or more ESTs each, correlated patterns of gene expression among libraries and genes were discerned. Two-dimensional qualitative representations of contig and library similarities were generated based on expression profiles. Genes with similar expression patterns and, potentially, similar functions were identified. These studies provide a rich source of publicly available gene sequences as well as valuable insight into the structure, function, and evolution of a model crop legume genome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".